Depression and anxiety are highly prevalent and frequently co-occur. Moreover, they are associated with functional impairment and physical health risks, including early mortality. Despite their significance to public health, treatment for depression and anxiety remains insufficient. Understanding of these illnesses, their comorbidity, and how to most effectively treat them has been limited by categorical approaches to diagnosis and research and by reliance on subjective reports. In addition, the field has historically focused on negative factors, failing to account for the unique effects of positive psychological processes. Positive affect, independent of negative affect, benefits both psychological and physical health. However, the biobehavioral processes by which positive affect confers these protective effects have not been fully elucidated. The Research Domain Criteria (RDoC) include both negative and positive valence domains and require use of multiple units of analyses (e.g., both self-report and neurological measures). RDoC thus offers a framework for advancing the science of affective processes that are relevant to both wellbeing and mental illness. Examination of relationships between imaging and self-report data is a critical next step. Our team has developed a novel method of data-driven techniques that can be applied to MRI data to allow quantitative investigation of affective processes. High-resolution structural connectome (HRSC) mapping using diffusion weighted imaging enables the analysis of single-subject and group-level structural connectivity at more than 50,000 points along the cortex and surfaces of the deep nuclei. HSRC mapping provides a unique opportunity to examine patterns of structural connectivity, as a means of advancing our understanding of the circuitry underlying self-reported affect. In response to PAR-17-158, ?Secondary Data Analyses to Explore NIMH Research Domain Criteria (R03),? we propose to analyze neuroimaging and self-report data from the Human Connectome Project (HCP), a publicly available dataset collected as part of the NIH Blueprint for Neuroscience Research. We will apply our innovative HRSC technique to examine constructs within the RDoC negative and positive valence domains. Using data from HCP participants (N=1206) who completed neuroimaging and NIH Toolbox measures of both negative and positive affect, we will examine the unique associations between negative and positive affect with brain structural connectivity, and evaluate whether positive affect moderates the associations between brain structural connectivity and negative affect. The results will provide a novel and quantitative understanding of psychophysiological processes underlying both negative and positive affect, across a broad range of emotion profiles. These methods and findings have long-term potential for contributing to the identification of neuroaffective predictors of psychiatric illness and response to treatment.